One subject I’ve often discussed with friends and colleagues is about making difficult decisions, the reasoning behind those decisions, and the personal characteristics involved. What are the hallmarks of making good decisions in a challenging environment or situation?

There are many factors but for this post I want to refute – with the help of a wonderful example I found today – one factor that I think is either over emphasized or perhaps not really a factor at all. That factor is: fearlessness.

The definitionis “to be free from fear”. If you are faced with a difficult decision whether in a personal sphere, in business, or other arena is a lack of fear a good thing? It certainly might help you get over the hump, so to speak, and to act upon a decision you might have based on your judgment, your morality or ethics, particularly if the consequences for failure are dire enough (e.g., failed business, failed relationship, even life-or-death).
This has always troubled me though. I just seems that as humans we allhave fears and given they are seemingly universal then those fears are there for some useful purpose (and we should be paying attention to them!).

SpaceX, the company founded by Elon Musk sent the Dragon spacecraft to the International Space Station

“I wouldn’t say I have a lack of fear. In fact, I’d like my fear emotion to be less because it’s very distracting and fries my nervous system. I have this sort of feeling that something terrible could happen, like all of our flights could fail and Tesla could fail and SpaceX could fail, and that feeling of anxiety has not left me, even though this has been a great year. So I feel fear quite strongly; I just proceed nonetheless.“

I would say that fear is part of what stokes Elon’s drive and his attention to what is important in his business decisions. Fear is a means but the end – the decision to and actually act – is all about bravery. To move ahead despite the risk, despite the fears is a concious act, not an unconcious act (as fearlessness seems to be, at least to me).

So, may you recognize your fears. Confront them and use them to inform your decisions. Then summon your bravery to act with confidence on the decisions you have made, or as Elon says “just proceed nonetheless“.

Teamwork, and probably more importantly, how teams work is an enduring and important topic. Much of how things get done in business, families and other societal groups, and life in general is through teams.

There is an interesting recent blog on Forbes, by Inder Sidhu who is a Senior VP of Planning and Strategy at Cisco, on the results of a study done on a charter school in Minnesota. The Avalon School has many characteristics that set it apart from other public schools: it has no principal, no full-time administrators and no director. They do not answer to a superintendant or district supervisors, rather the school’s educators make all decisions regarding budgeting, hiring, curricula and more.

In terms of individual team member satisfaction, evidence indicates working in this environment produces teachers that are more satisfied than peers at other schools. This is consistent with studies of other similarly run charter schools. This is a possibly important finding — the job satisfaction of teachers would logically have some impact on their performance in the classroom and possibly extend to the performance of students in their classrooms. This effect on students though had not been studied or shown until the Avalon School study.

The research was done by Claremont Graduate University professor Charles Taylor Kerchner. The study compared academic achievement of Avalon to other schools in its area. It found that Avalon was producing high performing students and also multidisciplined teachers. The evidence included outperforming other schools on federal Adequate Yearly Progress (AYP) requirements, in reading proficiency, and graduating a very high percentage of its students – 87% – with many going on to attend top tier universities such as Northwestern and the University of Michigan. There is much more but I’ll leave it to you to read the study (link above) if you wish.

There seems to be much that can be learned about the team working environment at Avalon. In a typical school a teacher’s job is clearly defined and their activities and routines are often overly prescribed. At Avalon, teachers are largely unrestricted and develop new skills in budgeting, hiring, marketing, recruiting, conflict resolution, and project management. Having to rely upon each other for goal setting, decision making and problem resolution rather than job and work rules and a central education authority and bureaucracy, the Avalon education team finds it easier to develop a common vision, set of objectives and hold themselves collectively accountable. In a charter school, lack of performance as a team can lead to the revocation of the charter and the school — and team — returning to the standard public school approach and work environment.

The teamwork atmosphere extends to the students. Seemingly taking the business management concept of “self managed teams” into a school setting, Avalon students discipline themselves. If an Avalon student gets into trouble, they are sent to a council of their peers — who have received training in peer mediation — for a hearing. Additionally, the students at Avalon determine most of the rules for the school. For self directed teams to work effectively, they must have this combination of lattitude in rule setting and governance, and accountability for group performance to the rules and standards.

It would be my belief that beyond the improved education the students at Avalon are getting, a powerful additional benefit is their learning about and experiencing a positive team working environment that will serve them well into their life and careers beyond Avalon.

Recently I had the privilege of teaching some classes at Emory University in the business school. I did a lot of preparation and reading about teaching in the university setting and particularly using the case method. One approach I read about and then adopted was to maximize the students’ participation by “orchestrating” the class, rather than lecturing or using a firm agenda and prepared questions (and prescribed answers).

We set some goals for what we wanted to get from the case, what we thought were key issues to be discussed and deliverables to be generated. Then I turned really into a conductor or director whose job was to facilitate, to keep us moving toward the goals, but not otherwise dictate. I found that like the Avalon experience, and experience where I’ve seen teams perform at their highest, the class thrived on the freedom and self-direction and had strong, innate drive to work together toward the goals.

What do you think? Do you have similar or varying experience with teams and teamwork?

In the first post about predictive analtyics we learned about the essential building block of predictive analytics: the predictor. This is a value calculated for each entity (say, a customer) who’s actions or behaviors are to be predicted – for instance the recency, in months, since a customer’s last purchase.

Prediction power is enhanced if you use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. In this post I’ll discuss how you can find the “best” predictive model. I put “best” in quotes because from a practical standpoint, unless you assume unlimited time and resources you may be best off finding a model that improves your results (e.g., reduction in customer churn) over previous experience. Today there is available very powerful modeling software and well-trained and talented statisticians, but the number of variables to consider in any predictive model (across demographics, transactions, behaviors) can be extremely large making determination of the “best” model cost prohibitive.

Fortunately, taking an incremental, continuous improvement approach can yield solid results for most any business and the promise that results will improve over time. A common tool is to develop a yield curve. For example, plotting the results of a predictive model for churn with amount of churn on the Y axis and percentage of customers contacted in a retention campaign on the X axis will show a curve the decreases to a point — i.e., up to a certain percentage of a universe of customers contacted, attrition rates will fall — but will bottom out and then move upward. Meaning that not all customers will respond to a retention campaign and you are best off contacting only those predicted to respond well. After that point, you are best leaving the balance of the universe of customers alone – either because they are not likely to churn anyway or because the predictive models say campaigns to retain them will be unsuccessful (and possibly other methods are needed – along with models that might predict how these approaches can be equally tuned to expend effort on just those predicted to be successful).

Now, although the model does not work perfectly, the socring and ranking of customers according to their likelihood to be retained provides clear guidance on how to invest in retention programs to yield the best results. It will prevent campaigns to retain customers that are too aggressive (trying to retain those that are not likely to respond positively, or wasting effort on those that are likely to stay).

There is a great deal more to predictive analytics than I’ve covered in the past two posts. But I hope one message is clear: you can gain practical improvements in marketing results or other customer touch points through the use of analytics that don’t need to be complex (at least to start) nor perfect. Commitment, willingness to experiment and continuous improvement are what’s really required.

Sorry for the delay between posts. For past month or so we’ve been working on a very interesting project dealing with product ideas based on financial transaction data and powered by predictive analytics. While we are working to develop some early prototypes we have also been talking about challenges that need to be addressed when taking such products to market.

One issue over and over has been risk of market launch failure due to lack understanding of how analytics work (often lacking even rudimentary let alone deep understanding). A majority of key stakeholders – potential customers and internal business unit and functional area team – have heard of and are relatively convinced of the potential for analytics to optimize decision making. Whether that be to improve marketing effectiveness or precision of sales forecasts. Yet the basis for belief is often what they’ve read about or been led to believe by others. Analytics are not perfect and an important approach to achieving long term benefits from analytics is experimentation, challenging current results, and continual tuning of analytical models. We can foresee a gap forming where confidence in what is being developed and sold to clients falters due to lack of basic understanding of predictive analytics.

So, I thought I’d put together a brief series of posts (sort of like I did on “Practical Strategy” a little while ago) to explain predictive analytics.

The essential building block of predictive analytics is the predictor. It is a value calculated for each entity to be predicted – for instance the recency, in months, since a customer’s last purchase. Typically, the higher the calculated recency the more recent was the last purchase. As you’d expect, a good predictor is usually a reliable variable that consistently improves accuracy of some decision or action. Such as “customers with a high recency value typically have a higher response rate to marketing programs.”

There are other predictors that might work better with certain actions or decisions. For example, if you have an online subscription-based service, customers who spend less time logged on are less likely to renew annually. Tuning attrition or churn reduction campaigns by targeting customers who have low usage predictor values can boost effectiveness.

To make prediction even more precise you can use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. Some simple models that might predict likelihood of a customer to renew their subscription:

– Behavioral Rules – joining two or more behaviors with rules defining predictions of another behavior. For example: Usage (high or low) and Responded to Offer in Past 3 Months.

The best predictors will be predictive models that combine multiple aspects of a customer (e.g., demographics) and their behavior. A predictive model characteristically must be deeper and more complex than the above examples – uniting sometimes dozens of predictors. More on determining the best predictive model and harnessing rich sources of data to create powerfully predictive analytics in the next post. Thanks for reading and let me know if you have comments or can share your own experiences.

This is the third in a series on developing a “practical strategy”. So far we’ve looked at two of the five basic questionsthat can be used as a framework for building and testing the strategy of an organization. The last three questions we’ll cover in this post.

The first two questions are “what business are we in?” and “where is the market going?” These questions serve to both build upon the vision which was developed (described in the first post ) and to test it in a practical way. The final three are “Where do we want to go?”, “How will we win?”, and “How will we get there?”. If the vision and the first two questions are for framing and testing then these last three are to useful for building out the details and getting ready to launch.

Picking up from the last post where the market landscape and strategic choices were developed the next step is to make those choices and identify the possible outcomes in order to be precise with the strategy. It is easy to be wishy-washy (sorry for use of such a jargon-laden term!) or settle for being too-high level. After all this is just the “strategy” and details can come later, right? Not right! Sure more details will come later in iterative execution phases and over time but forcing out specificity at this point is very valuable. Otherwise you can easily develop an elegant and logically sound strategy that still fails in the real world.

For example, while developing long term strategy at CheckFree, the leading provider of outsourced online banking and bill pay to U.S. financial institutions, the market — of both consumers who used it and the banks who provided it to them — was rapidly coming to accept such applications as mainstream (a classic sign of market “maturity”). But there was clear difference in the states of the two key market segments that made up the value chain for CheckFree. One segment, the bank market, was more mature and the competition was likely to force price into being a key competitive issue. Consumers, the other key segment, were still in the early stages of mainstream adoption. Plus a key variable was not simply adoption (what % of households were paying bills online) but penetration (what % of all household bills were being paid online — a sort of share of “bill payment wallet”).

The adoption metric was headed to and beyond 15% (and was at 30% at the leading bank in the U.S.) but the share of wallet was less than 5%. A clear choice on “where do we want to go?” was made: focus on the consumer. Clearly it seemed that there was both a significant unanswered challenge – how to get adopting households to pay all of their bills online through their bank — plus significant upside (increasing penetration offered a rich pool of latent, recurring revenue).

Turning to “how will we win?”: as with all of these questions they are best used in companion and with one another in an iterative manner. For instance, if we had chosen, instead, to give primacy to the bank market’s needs and compete on dimensions of traditional IT outsourcing — such as low cost, scale and quality — we felt we could win yet these were more mature areas and risk of commoditization was high (and price being a likely, and recurring, battleground). When we thought through our choice to compete with a consumer-focused strategy we were betting on this “pulling” through the banks and positioning us as clearly differentiated and preferred option in any competitive situation. The thinking was: if we could be the world-class experts in consumer adoption we were purposely choosing a more difficult yet competitively defensible path. We believed this competitive stratgegy would further raise switching costs and lock in market share with banks who chose us — and serve to help us avoid competing on price.

The last question, “How will we get there?” seems a little anticlimatic. This is by design. As I’ve mentioned previously a risk in developing strategy (amongst many!) can be that it is not practical (e.g., too high level, non-specific, hedges or is wishy-washy). If we’ve been thorough in answering and iterating through the vision and the first four questions the we’ll combat the impractical through the explicit development of a plan to accomplish the chosen strategy. The plan must include a clear set of discrete steps, time-phased and integrated across necessary functional or other organizational boundaries, assign specific accountable owners, and designate expected outcomes which become goals and metrics upon which to review and judge progress of the strategy execution and success of its outcomes. Wrappered around this methodology for developing practical strategy should be some sort of on-going strategic review, discussion and revision process (which I might blog about some other day). I like developing a 2 to 3 year strategy and then reviewing it every quarter on a rolling basis.

That’s it. I would welcome Comments from friends of my blog and from those just passing by and here for the first time. Randy

Following on from the previous post, and the second in this series on developing a “practical strategy”, there are five basic questions that can be used as a framework for building and testing the strategy for an organization. I will cover two of them in this post and the rest in a post or two over the next few weeks.

The first two are “what business are we in?” and “where is the market going?” These questions serve to both build upon the vision which was developed (described in the first post ) and to test it in a practical way. The thinking being: the vision has to not just read nicely and seem logical but you should be able to deconstruct it and determine its practical applicability.

For example, here’s a real-world vision statement: We help mid-size businesses improve their Pipeline-to-Profitability (“P2P”) cycle. Our business intelligence solutions are easy to use, offer immediate value and require minimal investment, using existing systemsand data sources. For this company, it was a significant turning point to re-define their business in this way. Previously they were more me-too as a business intelligence software provider delivering custom solutions in the “small to medium” (SMB) market. This was a good business but to grow it and to develop efficient marketing strategy and execution behind it was actually difficult because “what business are we in? “ resulted in an answer that was too broad and undifferentiated.

Above I underlined some key elements of their new vision. Each of these were chosen carefully and were backed by analysis, discussion and judgment to test whether they gave clear guidance about what business are they really in and whether data could be gathered which indicated where the market was going. As a product and marketing professional, having clear sets of facts and decisions about these two elements is a big advantage – and too-often they are not clearly available as marketing strategy is developed.

I’ll discuss just a couple of the key elements of the new vision from above to illustrate:

Mid-size – depending upon the definition of “small to medium size (SMB) business” there are at least 6.6 million (and some reports put the number at 20+ million if you include part time, SOHO and cash-only businesses) in the U.S. There’s a fair amount of hype about the potential for pursuing and selling to this somewhat untapped and very large B2B segment. Some iterative analysis and pondering of readily available data on this market showed us that the larger revenue size (what we came to call “mid-size”) businesses were more readily identifiable (e.g., segmented into industry categories) and still represented a significant market (625,000 in the U.S.).

Pipeline to Profitability – the company had developed some good off-the-shelf analytics that could be used by sales management to better understand their sales performance and provide insight that can improve effectiveness and results. The sales cycle though is a generic concept and varies widely across businesses due to product mix, complexity, price, market segments and channels. Some study of the marketplace indicated though that the sales pipeline – the narrow set of sales steps used to move a “qualified” prospect through to final sale – was a universal issue and the heart beat of any sales process. It was also well-defined and lent itself to simple analytics that yielded significant (i.e., high value) insights. Most importantly it was generally poorly served in terms of linking the management of the sales pipeline to profitable outcomes. Most solutions on the market totally ignored this critical component.

Immediate Value – later on you’ll learn that this was the chosen “key differentiator”. Every business or organization needs a key differentiator – ideally just one (that is so powerful that if well chosen and executed it is sufficient) to anchor the focus of the business, including technology and product investments, marketing messages and delivery or supply chain operations, as those apply. Much of what happens, or more accurately doesn’t happen, in business intelligence solutions and particularly in the CRM or sales arena is that value is not immediately delivered. Rather data (e.g., reports, alerts) is delivered slowly after much effort (and investment) and typically is not exactly (in form or content) what is needed by end users such as busy sales managers and executive managers. So rounds of iterations and alterations take place in search of the value and satisfaction required to ensure the solution will actually be deployed and used. This lag in achieving value – some call it ROI — and reasons for the lag are too numerous to go into here. It simply became clear that if value could be delivered “immediately” (the initial goal is within two days and long term goal is truly immediate) there was a void in the market and competitive differentiation could be clearly articulated and achieved.

Using these two questions in an iterative fashion is the best approach. Take each key element of the vision, ask: “what business are we in?” if we use that element. Then gather some external market data to ask further: “where is the market going?” relative to this key element – and you will rather rapidly shape, focus and finalize the business vision and also build up the fact-base behind it. This should give any business confidence that it is on the right track and once we are done with our five questions, should give the business confidence to pursue the entire vision with high energy and the proper amount of investment to achieve success.

True to my intent, I have written somewhat eclectically about discovering potential, looking ahead, thinking critically and objectively and wanted to get back to a business-oriented mode for a few posts.

Many organizations (and individuals!) are scratching their heads trying to figure out how to deal with our current, unique and challenging circumstances. But they are also trying to plan for the future (with optimism that “this too shall pass” and wanting to be ready for the next set of opportunities and challenges). I applaud any form of optimism! And so, I have a practical tool for use in getting some strategic thinking and planning done, which seems especially useful in these times as an overdone, over-wrought approach will be overkill when “directionally correct” might be all that is needed until some of the uncertainties and issues of the current time pass. I would argue though that even in more certain times, the approach I’ll write about in this and subsequent posts is useful and gets most any organization beyond being stuck in the present and looking ahead with a critical and purposeful eye.

The approach I advocate is squarely focused on getting a specific vision and strategy down on paper — and will serve as a very powerful tool to also use in successive iterations (a critical component of the strategy process as a one-time vision and strategy exercise might not even be worth the effort).

What I also like about this approach is that it uses language and key words that were not “strategy double-speak” and won’t put off the executives and other participants who often tune out of a strategy exercise because of preconceived notions about strategy, consultants, etc. (i.e., “too complicated”, “too high level”, “not executable”).

The approach also ensures completeness without being overly complex and strenuous as a management team exercise. I often say when about to embark on this process that I want the team to “work out”, not “wear out”, their thinking capacity.

I call it Practical Strategy because of the definition of the word “practical”: \ˈprak-ti-kəl\, adj., useful and no-nonsense.

There are two basic steps to the process, with the second working through and answering a series of questions. I’ll summarize the first step in this post, and then work through the second part and the questions in a couple of subsequent posts. The first step is to articulate a long range vision for the business. This can sound too simple on the surface. A good vision is not just a statement that gets put onto posters, inside annual reports, or laminated on cards handed out to employees and customers. Getting it right is hard work but needn’t be a too-long effort. It must be clear, specific and define the place for the business to aspire reaching (but with no set time horizon). A test will be that a good vision statement can be decomposed and set the boundaries for and guide the answering of the subsequent questions in this exercise. If it fails this basic test, the vision is not practical and should be refined.

I’ll give an example. The practical vision for Domino’s Pizza: “Make and deliver a fresh, hot, high-quality pizza to the customer’s home within 30 minutes or less.” Several things:
– this makes clear what value is to be delivered – fresh, hot and high-quality. Any one of these may be sufficient, why choose all three? Knowing why make subsequent decisions about business model, operational strategies and so forth quite clear

– a key differentiator is articulated – 30 minutes or less (and in their advertising they backed this with a guarantee-or-free offer)

– a key operational characteristic is defined – to the customer’s home. If taken literally (which they did), this kept them focused on the home delivery model and away from building sit-down or walk-in or stores, and has clear direction for their location and logistics strategies.

– even the omission of something can be useful — the vision only mentions pizza. No mention of other products or open-ended placeholders for other foods or items that could be thrown in. It is about pizza, plain and simple.

Not all businesses are as simple as Domino’s. Or is it that not all businesses go to trouble of defining their businesses in such clear and practical ways? I’m sure the answer is in the middle somewhere but I will argue it falls toward the latter.

As always I welcome your feedback and look for a post soon on the first of the questions that must be answered to complete the rest of the Practical Strategy process.